The K Nearest Neighbors (KNN) classifier is widely used in many fields such as fingerprint-based localization or medicine. It determines the class membership of an unlabelled sample based on the class memberships of the K labelled samples, the so-called nearest neighbors, that are closest to the unlabelled sample. The choice of K has been the topic of various studies and proposed KNN-variants. Yet no variant has been proven to outperform all other variants. In this paper a new KNN-variant is proposed, which ensures that the K nearest neighbors are indeed close to the unlabelled sample and finds K along the way. The proposed algorithm is tested and compared to the standard KNN in theoretical scenarios and for indoor localization based on ion-mobility spectrometry fingerprints. It achieves a higher classification accuracy than the KNN in the tests, while having the same computational demand.

The aim was to study if odors evaporated by an olfactory display prototype can be used to affect participants' cognitive and emotionrelated responses to audio-visual stimuli, and whether the display can benefit from objective measurement of the odors. The results showed that odors and videos had significant effects on participants' responses. For instance, odors increased pleasantness ratings especially when the odor was authentic and the video was congurent with odors. The objective measurement of the odors was shown to be useful. The measurement data was classified with 100% accuracy removing the need to speculate whether the odor presentation apparatus is working properly.

Localization accuracy of trilateration methods in long term evolution (LTE) cellular networks, which are based on time-of-arrival, may be highly degraded due to multipath and non-line of sight conditions in urban and indoor environments. Multipath mitigation techniques usually involve a high computational burden and require wideband signals to be effective, which limit their adoption in certain low-cost and low-power mobile applications using narrow-band signals. As an alternative to these conventional techniques, this paper analyzes an expectation maximization (EM) localization algorithm that considers the skewness introduced by multipath in the LTE ranging error distribution. The EM algorithm is extensively studied with realistic emulated LTE signals of 1.4-MHz bandwidth. The EM method is compared with a standard nonlinear least squares (NLS) algorithm under ideal simulated conditions and using realistic outdoor measurements from a laboratory testbed. The EM method outperforms the NLS method when the ranging errors in the training and test stages have similar distributions.

Low-complexity Bayesian filtering for nonlinear models is challenging. Approximative methods based on Gaussian mixtures (GM) and particle filters are able to capture multimodality, but suffer from high computational demand. In this paper, we provide an indepth analysis of a generalized GM (GGM), which allows component weights to be negative, and requires significantly fewer components than the traditional GM for ranging models. Based on simulations and tests with real data from a network of UWB nodes, we show how the algorithm's accuracy depends on the uncertainty of the measurements. For nonlinear ranging the GGM filter outperforms the extended Kalman filter (EKF) in both positioning accuracy and consistency in environments with uncertain measurements, and requires only slightly higher computational effort when the number of measurement channels is small. In networks with highly reliable measurements, the GGM filter yields similar accuracy and better consistency than the EKF.

A Bayesian method for dynamical off-line estimation of the position and path loss model parameters of a WLAN access point is presented. Two versions of three different on-line positioning methods are tested using real data. The tests show that the methods that use the estimated path loss parameter distributions with finite precisions outperform the methods that only use point estimates for the path loss parameters. They also outperform the coverage area based positioning method and are comparable in accuracy with the fingerprinting method. Taking the uncertainties into account is computationally demanding, but the Gauss-Newton optimization method is shown to provide a good approximation with computational load that is reasonable for many real-time solutions.